Search Results for author: Yassir Bendou

Found 5 papers, 4 papers with code

Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning

1 code implementation24 Nov 2023 Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene

In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class.

Few-Shot Learning

Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach

1 code implementation16 Jan 2023 Yassir Bendou, Lucas Drumetz, Vincent Gripon, Giulia Lioi, Bastien Pasdeloup

Then, we introduce a downstream classifier meant to exploit the presence of multiple objects to improve the performance of few-shot classification, in the case of extreme settings where only one shot is given for its class.

EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients

2 code implementations24 Jan 2022 Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stéphane Pateux, Vincent Gripon

Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available.

Few-Shot Image Classification Few-Shot Learning

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